22 Lessons
20 Students
Installation instructions were dated. Easier to follow examples would be better
A good use case of Numpy is quick experimentation and small projects because Numpy is a light weight framework compared to PyTorch. Moreover, PyTorch lacks a few advanced features as you'll read below so it's strongly recommended to use numpy in those cases .
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What You'll Learn
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Master fundamental PyTorch tensor operations for machine learning and deep learning applications
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Implement neural network models using PyTorch for real-world AI projects and data analysis
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Utilize PyTorch autograd for efficient automatic differentiation in deep learning workflows
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Develop custom loss functions and optimization algorithms with PyTorch for improved model performance
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Apply PyTorch data loading and preprocessing techniques for scalable machine learning pipelines
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Debug and troubleshoot PyTorch models to enhance accuracy and reliability in AI solutions
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Integrate PyTorch with other Python libraries like NumPy for seamless data manipulation and experimentation
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Deploy trained PyTorch models into production environments for practical AI applications
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Understand PyTorch's architecture to optimize model training and inference speed
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Leverage PyTorch tutorials and community resources for continuous learning and project development
Course Content
22 Lessons • 6 h 51 m total
PyTorch Tutorial 01 Installation
PyTorch Tutorial 02 Tensor Basics
PyTorch Tutorial 03 Gradient Calculation With Autograd
PyTorch Tutorial 04 Backpropagation Theory With Example
PyTorch Tutorial 05 Gradient Descent with Autograd and Backpropagation
PyTorch Tutorial 06 Training Pipeline Model Loss and Optimizer
PyTorch Tutorial 07 Linear Regression
PyTorch Tutorial 08 Logistic Regression
PyTorch Tutorial 09 Dataset and DataLoader Batch Training
PyTorch Tutorial 10 Dataset Transforms
PyTorch Tutorial 11 Softmax and Cross Entropy
PyTorch Tutorial 12 Activation Functions
PyTorch Tutorial 13 Feed Forward Neural Network
PyTorch Tutorial 14 Convolutional Neural Network CNN
PyTorch Tutorial 15 Transfer Learning
PyTorch Tutorial 16 How To Use The TensorBoard
PyTorch Tutorial 17 Saving and Loading Models
Create Deploy A Deep Learning App PyTorch Model Deployment With Flask Heroku
PyTorch RNN Tutorial Name Classification Using A Recurrent Neural Net
PyTorch Tutorial RNN LSTM GRU Recurrent Neural Nets
PyTorch Lightning Tutorial Lightweight PyTorch Wrapper For ML Researchers
PyTorch LR Scheduler Adjust The Learning Rate For Better Results
How to Get The Certificate
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Complete all lessons.
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Watch at least 50% of Lesson Duration.
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PyTorch Complete Beginner Course Description
A good use case of Numpy is quick experimentation and small projects because Numpy is a light weight framework compared to PyTorch. Moreover, PyTorch lacks a few advanced features as you'll read below so it's strongly recommended to use numpy in those cases .
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